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Influence of Thick Plate Bending Process on Material Strength Distribution in Hydrogenation Reactor Shells

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Abstract

Thick plate bending process (warm bending and tempering) has a profound impact on the material strength distribution (MSD) in hydrogenation reactor shells. To date, few studies have studied the thick plate bending process. In this work, an artificial neural network (ANN) combined with finite element analysis (FEA) was utilized to investigate the impact of thick plate bending on the MSD of reactor shells. First, tensile tests of 0-10% pre-strained 2.25Cr-1Mo-0.25 V specimens were subjected to 390 to 510 °C. The results obtained from this experiment were used to develop ANN with two inputs (temperature and plastic strain) to predict the strength of pre-deformed steel. Subsequently, the plastic strain distribution of reactor shells after warm bending was obtained via FEA. We then inputted the FEA results into well-established ANN to predict the MSD of un-tempered reactor shells. The MSD of an actual tempered reactor shell was measured to study the synergic effect of warm bending and tempering on MSD variation. Results showed that the average absolute relative errors between the proposed ANN and tensile test results were below 4%. The absolute relative errors of the proposed prediction method varied from 0.24 to 7.88%. The proposed method is therefore reliable in the lightweight design of the hydrogenation reactor.

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References

  1. D. Wang, Y. Li, L. Jin, K. Hao, B. Wei, D. Yao, and H. Haoquan, Integrated process for partial oxidation of heavy oil and in situ reduction of red mud, Appl. Catal. B, 2019, 258(10), p 968–973 (in English)

    Google Scholar 

  2. G.H.C. Prado, Y. Rao, and A. de Klerk, Nitrogen removal from oil: a review, Energy Fuels, 2017, 31(1), p 14–36 (in English)

    CAS  Google Scholar 

  3. Y. Zhang, L. Huang, X. Xi, W. Li, G. Sun, S. Gao, and S. Zhang, Deep conversion of venezuela heavy oil via integrated cracking and coke gasification − combustion process, Energy Fuels, 2017, 31(9), p 9915–9922 (in English)

    CAS  Google Scholar 

  4. Y. Zhang, Y. Deping, W. Li, Y. Wang, S. Gao, and X. Guangwen, Fundamentals of petroleum residue cracking gasification for coproduction of oil and syngas, Ind. Eng. Chem. Res., 2012, 51(46), p 15032–15040 (in English)

    CAS  Google Scholar 

  5. S. Huang, Z. Chen, Y. Li, and D. Zhang, Impact of hot bending forming on the high temperature mechanical properties and hydrogen damage of 2.25Cr-1Mo-0.25 V steel, J. Mater. Eng. Perform., 2019, 28(1), p 567–577 (in English)

    CAS  Google Scholar 

  6. H. Zhou, X. Chi, Q. Wang, H. Pan, and J. Chen, Characterization of hydrogen embrittlement by eddy current method, Res. Nondestr. Eval., 2018, 29(4), p 212–220 (in English)

    Google Scholar 

  7. Y. Liu and Q. Pang, Study on endurance property of 2.25Cr-1Mo-0.25 V steel hydrogenation cylinder forgings, Hot Work Technol., 2018, 47(1), p 158–160 (164, in Chinese)

    Google Scholar 

  8. Y. Wang, G. Cheng, M. Qin, Q. Li, Z. Zhang, K. Chen, Y. Li, H. Hu, W. Wu, and J. Zhang, Effect of high temperature deformation on the microstructure, mechanical properties and hydrogen embrittlement of 2.25Cr-1Mo-0.25 V steel, Int. J. Hydrog. Energy, 2017, 42(38), p 24549–24559 (in English)

    CAS  Google Scholar 

  9. Y. Song, M. Chai, B. Yang, Z. Han, S. Ai, Y. Liu, G. Cheng, and Y. Li, Investigation of the influence of pre-charged hydrogen on fracture toughness of as-received 2.25Cr1Mo0.25 V steel and weld, Materials, 2018, 11(7), p 1068 (in English)

    Google Scholar 

  10. Y. Song, M. Chai, B. Yang, Z. Han, S. Ai, Y. Liu, G. Cheng, and Y. Li, Effect of cementite on the hydrogen diffusion/trap characteristics of 2.25Cr-1Mo-0.25 V steel with and without annealing, Materials, 2018, 11(5), p 788 (in English)

    Google Scholar 

  11. Y. Song, M. Chai, W. Wu, Y. Liu, M. Qin, and G. Cheng, Experimental investigation of the effect of hydrogen on fracture toughness of 2.25Cr-1Mo-0.25 V steel and welds after annealing, Materials, 2018, 11(4), p 499 (in English)

    Google Scholar 

  12. Z. Jiang, P. Wang, D. Li, and Y. Li, The evolutions of microstructure and mechanical properties of 2.25Cr-1Mo-0.25 V steel with different initial microstructures during tempering, Mater. Sci. Eng. A-Struct. Mater. Properties Microstruct. Process., 2017, 699, p 165–175 (in English)

    CAS  Google Scholar 

  13. Z. Jiang, P. Wang, D. Li, and Y. Li, Effects of tempering temperature on the microstructure and mechanical properties of granular bainite in 2.25Cr-1Mo-0.25 V steel, Acta Metall. Sin., 2015, 51(8), p 925–934 (in Chinese)

    CAS  Google Scholar 

  14. R.D. Fu, S.F. Wu, W.H. Zhou, W.H. Zhang, Y.Q. Yang, and F.C. Zhang, Effects of heat treatment processes on microstructure and properties of 2·25Cr–1Mo–0·25 V HSLA steels, Metal Sci. J., 2013, 25(1), p 50–55 (in English)

    Google Scholar 

  15. Z. Hua, X. Zhang, J. Zheng, C. Gu, T. Cui, Y. Zhao, and W. Peng, Hydrogen-enhanced fatigue life analysis of Cr–Mo steel high-pressure vessels, Int. J. Hydrog. Energy, 2017, 42(16), p 12005–12014 (in English)

    CAS  Google Scholar 

  16. C. Wu, B. Jia, and S. Han, Coupling a cellular automaton model with a finite element model for simulating deformation and recrystallization of a low-carbon micro-alloyed steel during hot compression, J. Mater. Eng. Perform., 2019, 28(2), p 938–955 (in English)

    CAS  Google Scholar 

  17. C. Liu and J. Gao, Stress corrosion of hydrogenation reactor material, J. Northeast For. Univ., 2009, 37(4), p 65–57 (in Chinese)

    Google Scholar 

  18. O. Sabokpa, A. Zarei-Hanzaki, H.R. Abedi, and N. Haghdadi, Artificial neural network modeling to predict the high temperature flow behavior of an AZ81 magnesium alloy, Mater. Des., 2012, 39, p 390–396 (in English)

    CAS  Google Scholar 

  19. H. Ahmed, M.A. Wells, D.M. Maijer, B.J. Howes, and M.R. van der Winden, Modelling of microstructure evolution during hot rolling of AA5083 using an internal state variable approach integrated into an FE model, Mater. Sci. Eng., A, 2005, 390, p 278–290 (in English)

    Google Scholar 

  20. S.B. Davenport, N.J. Silk, C.N. Sparks, and C.M. Sellars, Development of constitutive equations for modelling of hot rolling, Mater. Sci. Technol., 2000, 16, p 539–546 (in English)

    CAS  Google Scholar 

  21. J. Luo, M. Li, X. Li, and Y. Shi, Constitutive model for high temperature deformation of titanium alloys using internal state variables, Mech. Mater., 2010, 42, p 157–165 (in English)

    Google Scholar 

  22. Y. Xia, W. Jiang, Q. Cheng, L. Jiang, and L. Jin, Hot deformation behavior of Ti-6Al-4 V-0.1Ru alloy during isothermal compression, Trans. Nonferrous Metals Soc. China, 2020, 30(1), p 134–146

    CAS  Google Scholar 

  23. Y. Han, G. Qiao, J. Sun, and D. Zou, A comparative study on constitutive relationship of as-cast 904L austenitic stainless steel during hot deformation based on Arrhenius-type and artificial neural network models, Comput. Mater. Sci., 2013, 67, p 93–103 (in English)

    CAS  Google Scholar 

  24. Z. Sun, H. Yang, and Z. Tang, Microstructural evolution model of TA15 titanium alloy based on BP neural network method and application in isothermal deformation, Comput. Mater. Sci., 2010, 50(2), p 308–318 (in English)

    CAS  Google Scholar 

  25. G. Quan, W. Lv, Y. Mao, Y. Zhang, and J. Zhou, Prediction of flow stress in a wide temperature range involving phase transformation for as-cast Ti–6Al–2Zr–1Mo–1 V alloy by artificial neural network, Mater. Des., 2013, 50, p 51–61 (in English)

    CAS  Google Scholar 

  26. Y. Zhu, W. Zeng, Y. Sun, F. Feng, and Y. Zhou, Artificial neural network approach to predict the flow stress in the isothermal compression of as-cast TC21 titanium alloy, Comput. Mater. Sci., 2011, 50, p 1785–1790 (in English)

    CAS  Google Scholar 

  27. Z. Cai, H. Ji, W. Pei, X. Tang, L. Xin, L. Yonghao, and W. Li, An investigation into the dynamic recrystallization (DRX) behavior and processing map of 33Cr23Ni8Mn3N based on an artificial neural network (ANN), Materials, 2020, 13(6), p 1282 (in English)

    CAS  Google Scholar 

  28. X. Wu, H. Zhang, H. Cui, Z. Ma, W. Song, W. Yang, L. Jia, and H. Zhang, Quantitative relationship analysis of mechanical properties with mg content and heat treatment parameters in Al–7Si alloys using artificial neural network, Materials, 2019, 12(5), p 718 (in English)

    CAS  Google Scholar 

  29. Y. Zhou, Y. Xia, L. Jiang, S. Long, and D. Yang, Modeling of the hot flow behaviors for Ti-6Al-4 V-0.1Ru alloy by GA-BPNN model and its application, High Temp. Mater. Process (London), 2018, 37(6), p 551–562 (in English)

    Google Scholar 

  30. G. Quan, J. Pan, and X. Wang, Prediction of the hot compressive deformation behavior for superalloy nimonic 80A by BP-ANN model, Appl. Sci, 2016, 6(3), p 66 (in English)

    Google Scholar 

  31. Metallic materials-tensile testing-part 2: method of test at elevated temperature, GB/T 228.2, Chinese Standard, 2015

  32. P. Tao, C. Zhang, and Z. Yang, Evolution of second phase in 2.25Cr-1Mo-0.25 V steel weld metal during high temperature tempering, Acta Metall. Sin., 2009, 45(1), p 51–57 (in Chinese)

    CAS  Google Scholar 

  33. H. White, Learning in artificial neural networks: a statistical perspective, Neural Comput., 2014, 1(4), p 425–464 (in English)

    Google Scholar 

  34. S. Dreiseitla and L. Ohno-Machadob, Logistic regression and artificial neural network classification models: a methodology review, J. Biomed. Inform., 2002, 35(5–6), p 352–359 (in English)

    Google Scholar 

  35. Y. Gao, Fabrication of plate-welding type hydro-reactor made of 2.25Cr-1Mo-0.25 V steel, Press. Vessel Technol., 2006, 23(12), p 33–36 (in Chinese)

    Google Scholar 

  36. Q. Yue, Analysis for the plate bending process of the four-roll plate bending machine, Lanzhou Jiaotong University, 2014, in Chinese

  37. A. Ktari, Z. Antar, N. Haddar, and K. Elleuch, Modeling and computation of the three-roller bending process of steel sheets, J. Mech. Sci. Technol., 2012, 26(1), p 123–128 (in English)

    Google Scholar 

  38. X. Han and L. Hua, 3D FE modeling of cold rotary forging of a ring workpiece, J. Mater. Process. Technol., 2009, 209(1–2), p 5353–5362 (in English)

    CAS  Google Scholar 

  39. G.Y. Zhao, Y.L. Liu, H. Yang, C.H. Lu, and R.J. Gu, Three-dimensional finite-elements modeling and simulation of rotary-draw bending process for thin-walled rectangular tube, Mater. Sci. Eng. A-Struct. Mater. Prop. Microstruct. Process., 2009, 499(1–2), p 257–261 (in English)

    Google Scholar 

  40. S. Huang, Z. Chen, Y. Li, and D. Zhang, Effect of warm forming on microstructure and mechanical properties of 2.25Cr1Mo0.25 V steel cylindrical shell, Heat Treat. Met., 2019, 44(5), p 77–82 (in Chinese)

    Google Scholar 

  41. Z. Liu, C. Liu, L. Miao, X. Guo, J. Ding, and H. Zhang, The evolution of complex carbide precipitates in a low alloy Cr-Mo-V steel after long-term aging treatment, Materials, 2019, 12(10), p 1724 (in English)

    CAS  Google Scholar 

  42. R.D. Fu, Y.Q. Yang, C.Y. Wang, and W.H. Zhang, Effects of weld thermal cycles and post-welding tempering on second phase particles in 2·25Cr–1Mo–0·25 V steels, Sci. Technol. Weld. Joining, 2008, 13(4), p 349–356 (in English)

    CAS  Google Scholar 

  43. L. Song, R. Sun, W. Gu, and X. Li, Effects of heat treatment process on microstructure and mechanical properties of 2.25Cr-1Mo-0.25 V steel weld seam, Heat Treat. Met., 2015, 40(12), p 110–113 (in Chinese)

    CAS  Google Scholar 

  44. D. Wu, F. Wang, C. Jin, and C. Li, Effects of Nb and tempering time on carbide precipitation behavior and mechanical properties of Cr-Mo-V steel for brake discs, Steel Res. Int., 2018, 89(5), p 1700491 (in English)

    Google Scholar 

  45. X. Liu, S. Nie, and Y. ren, Effects of predeformation diffusion annealing on distribution of alloy agents and structures, J. Plast. Eng., 2007, 14(6), p 138–141 (in English)

    CAS  Google Scholar 

  46. J. Zhang, W. Zhang, F. Ruidong, and C. Wang, Hot deforming behaviors and microstructures of 2.25Cr-1Mo-0.25 V steels, J. Plast. Eng., 2010, 17(3), p 44–49 (in Chinese)

    Google Scholar 

  47. D. Sylvain, T.M. Caroline, U. Stephane, R. Justine, M. Bernard, R. Francois, K. Ernst, G. Lorenzon, and A. Francoise, Carbide precipitation in 2.25Cr-1Mo bainitic steel: effect of heating and isothermal tempering conditions, Metall. Mater. Trans. A-Phys. Metallurgy Mater. Sci., 2017, 48A(5), p 2164–2178 (in English)

    Google Scholar 

  48. M. Qin, G. Cheng, Z. Zhang, Q. Li, J. Zhang, Numerical simulation of temperature field and residual stress in multi-pass welds in 2.25Cr-1Mo-0.25 V steel plate and comparison with experimental measurements, in Proceedings of the Asme Pressure Vessels And Piping Conference, Waikoloa, HI, Jul 2017

  49. P. Tao, C. Zhang, Z. Yang, and H. Takeda, Evolution and coarsening of carbides in 2.25Cr-1Mo steel weld metal during high temperature tempering, J. Iron Steel Res., 2010, 17(5), p 74–78 (in English)

    CAS  Google Scholar 

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Acknowledgments

This work was supported by National Key Basic Research and Development Project of China (973 Project No. 2015CB057603) and National Natural Science Foundation of China (Grant No. 51905173).

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Authors and Affiliations

  1. Institute of Process Equipment, Zhejiang University, 38 Zheda Road, Hangzhou, 310027, Zhejiang, People’s Republic of China

    You Li, Zhiping Chen, Peng Jiao, Delin Zhang, Dong Xu & He Ma

  2. School of Mechanical and Power Engineering, East China University of Science and Technology, Shanghai, 200237, People’s Republic of China

    Song Huang

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  1. You Li
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Correspondence to Zhiping Chen.

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Li, Y., Chen, Z., Jiao, P. et al. Influence of Thick Plate Bending Process on Material Strength Distribution in Hydrogenation Reactor Shells. J. of Materi Eng and Perform 29, 5158–5173 (2020). https://doi.org/10.1007/s11665-020-05020-4

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